Given that character-level \(n\) -gram could reduce the effect of spelling errors and variations in tweets [1]}, we used a bag of character \(n\) -grams (with \(n\)  ranging from 2 to 7 characters) as features in order to characterize the users' language features as robustly as possible. Since in subtask C, we need to identify different types of offense target, we assume that named entity information will be effective for identifying target types. Named entities information was extracted by spaCy, which is based on the entity types from OntoNotes 5 corpushttps://spacy.io/api/annotation#named-entities. Given that this task aims to identify three types of targets, namely individual, group and other, we used the named entity information by classifying all the entity types into three major types and counting the number of each type separately. The first type only includes PERSON entities, the second type consists of entity types related to a group sense, for example ORG, NORG, and GPE, and the last type includes all occurrences of the other entity types.
